Algorithm complexity measures the efficiency of an algorithm in terms of time and space resources. It helps developers understand how an algorithm will perform as input size grows. Here's a breakdown:

Key Concepts

  • Time Complexity: How long an algorithm takes to run based on input size
    ⏱️ Example: Sorting algorithms like Bubble Sort have higher time complexity than Quick Sort

    Time_Complexity

  • Space Complexity: How much memory an algorithm requires
    🧱 Example: Recursive algorithms often have higher space complexity due to call stacks

    Space_Complexity

  • Big O Notation: Standardized way to describe complexity
    📈 Common types:

    • O(1) - Constant time
    • O(log n) - Logarithmic time
    • O(n) - Linear time
    • O(n²) - Quadratic time
      Big_O_Notation

Comparison Table

Algorithm Time Complexity Space Complexity
Binary Search O(log n) O(1)
Merge Sort O(n log n) O(n)
Bubble Sort O(n²) O(1)
Hash Table Lookup O(1) O(n)

For deeper exploration, check our guide on Time Complexity Analysis. Want to visualize complexity types? See Big O Examples for interactive diagrams.